Abstract-The electromagnetic scattering of the synthesized threedimensional (3-D) breaking wave crests which are formed by azimuthally aligning the individual 2-D breaking wave profiles has been numerically studied at the low-grazing angles (LGA) by using the multilevel fast multipole algorithm (MLFMA) with adaptive higher order hierarchical Legendre basis functions. Different from the specular (or quasi-specular) reflection and Bragg scattering, the "sea-spike" phenomenon which is characterized by that horizontally polarization (HH) signals greatly exceed vertically polarization (V V ) signals has been demonstrated by analyzing both the backscattering of 3-D LONGTANK series and a plunging breaker. For the timedependent evolution of the plunging breaker, the Doppler shifts and Doppler splitting effects are investigated by applying the fast Fourier transform (FFT) with a moving Hamming window. The spectrum of HH scattering has the feature of concentration, while the spectrum of V V scattering shows the Doppler splitting effects.

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Abstract-An iterative hybrid method combining the Kirchhoff approximation (KA) and the multilevel fast multipole algorithm (MLFMA) is studied for electromagnetic scattering from a threedimensional (3-D) object above a two-dimensional (2-D) random dielectric rough surface. In order to reduce the computational costs, some treatments have been studied. Firstly, the fast far-field approximation (FAFFA) is utilized to speed up the electromagnetic coupling interaction process between the rough surface and the object. Secondly, based on the scattering mechanism of the rough surface, a truncation rule on moderate rough surface for bi-static scattering is proposed under the plane wave illumination, which can further speed up the iteration. Compared with the conventional methods, the hybrid method with the above treatments is very efficient to analyze the scattering of a 3-D object above random rough surfaces. Simulation results validate the effectiveness and accuracy of the iterative hybrid method.

We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN) to focus the distorted inverse synthetic aperture radar (ISAR) image. In this paper, a coefficient estimator based on the artificial neural network (ANN) is firstly developed to solve the time-consuming rotational motion compensation (RMC) polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs). Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging.

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